Automatic Segmentation and Quantitative Measurement of Deep Medullary Veins Diameter

Motivation: Deep medullary veins (DMVs) stenosis may be one of the causes of small vessel disease, so non-invasive tool for its assessment is desired. Goal(s): Developing automatic DMV segmentation and diameter quantification methods for assessing DMV stenosis. Approach: We trained an automatic segmentation model and proposed a DMV diameter quantification method by analyzing the complex MRI signals at sub-voxel scale. Results: The segmentation model achieved satisfactory performance. The accuracy of the diameter quantification method was verified in phantoms. The fitted DMV diameter distribution was close to earlier ex-vivo report and showed strong correlation with DMV susceptibility from quantitative susceptibility mapping. Impact: Our approach can serve as a useful automatic pipeline to study the role of DMV stenosis in the pathogenesis of small vessel disease.